Factor augmented VAR revisited - A sparse dynamic factor model approach
Simon Beyeler and
Sylvia Kaufmann ()
No 16.08, Working Papers from Swiss National Bank, Study Center Gerzensee
We combine the factor augmented VAR framework with recently developed estimation and identification procedures for sparse dynamic factor models. Working with a sparse hierarchical prior distribution allows us to discriminate between zero and non-zero factor loadings. The non-zero loadings identify the unobserved factors and provide a meaningful economic interpretation for them. Given that we work with a general covariance matrix of factor innovations, we can implement different strategies for structural shock identification. Applying our methodology to US macroeconomic data (FRED QD) reveals indeed a high degree of sparsity in the data. The proposed identification procedure yields seven unobserved factors that account for about 52 percent of the variation in the data. We simultaneously identify a monetary policy, a productivity and a news shock by recursive ordering and by applying the method of maximizing the forecast error variance share in a specific variable. Factors and specific variables show sensible responses to the identified shocks.
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Working Paper: Factor augmented VAR revisited - A sparse dynamic factor model approach (2019)
Working Paper: Factor augmented VAR revisited - A sparse dynamic factor model approach (2018)
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